Spatial Transform.
A key module of 3D lane detection is the spatial transformation from 2D feature to 3D feature. The spatial transform module[1]} is a trainable module that is flexibly inserted into the CNN to implement the corresponding spatial transformation of the input feature map, and is suitable for converting 2D space feature into 3D geometric feature information. In the 3D lane detection, there are four kinds of commonly used spatial transform modules,such as IPM-based methods [2]}, [3]}, [4]}, but this method relies strongly on the camera intrinsic and extrinsic parameters and ignores the ground surface undulations and vehicle shaking. The MLP-based method [5]}, [6]} has robust performance and is easy to deploy, but it is a fixed spatial mapping, which are difficult to be integrated with the camera intrinsic and extrinsic parameters. Transformer-based spatial transformation module[7]}, [8]} has better robustness, but it is not easy to deploy due to the large amount of computation. The space transformation based on deepth[9]}, [10]} has a large amount of calculation and thus is not suitable for deployment.
